Trans-biobank analysis with 676,000 individuals elucidates the association of polygenic risk scores of complex traits with human lifespan

Abstract

While polygenic risk scores (PRSs) are poised to be translated into clinical practice through prediction of inborn health risks1, a strategy to utilize genetics to prioritize modifiable risk factors driving heath outcome is warranted2. To this end, we investigated the association of the genetic susceptibility to complex traits with human lifespan in collaboration with three worldwide biobanks (ntotal = 675,898; BioBank Japan (n = 179,066), UK Biobank (n = 361,194) and FinnGen (n = 135,638)). In contrast to observational studies, in which discerning the cause-and-effect can be difficult, PRSs could help to identify the driver biomarkers affecting human lifespan. A high systolic blood pressure PRS was trans-ethnically associated with a shorter lifespan (hazard ratio = 1.03[1.02–1.04], Pmeta = 3.9 × 10−13) and parental lifespan (hazard ratio = 1.06[1.06–1.07], P = 2.0 × 10−86). The obesity PRS showed distinct effects on lifespan in Japanese and European individuals (Pheterogeneity = 9.5 × 10−8 for BMI). The causal effect of blood pressure and obesity on lifespan was further supported by Mendelian randomization studies. Beyond genotype–phenotype associations, our trans-biobank study offers a new value of PRSs in prioritization of risk factors that could be potential targets of medical treatment to improve population health.

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Fig. 1: An overview of the study design.
Fig. 2: The HRs for the age at death, according to clinical phenotypes and according to the PRSs and their correlations in BBJ.
Fig. 3: The standardized survival rate, according to sBP and albumin, and the PRS status of both traits in BBJ.
Fig. 4: Trans-ethnic association study of biomarker PRSs with lifespan.

Data availability

The genotype data of BBJ used in this study are available from the Japanese Genotype-phenotype Archive (http://trace.ddbj.nig.ac.jp/jga/index_e.html) with the accession code JGAD00000000123. The GWAS summary statistics for BBJ are available at the National Bioscience Database Center Human Database with the accession code hum0014. The UKB analysis was conducted via the application 31063, and its GWAS summary statistics are available at http://www.nealelab.is/uk-biobank. This study used the FinnGen release 3 data. Summary statistics from FinnGen are available on request from the FinnGen project and are being prepared for public release in May 2020.

Code availability

We used publicly available software for the analyses. The software programs used are listed and described in the Methods.

References

  1. 1.

    Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 1219–1224 (2018).

  2. 2.

    The All of Us Research Program Investigators. The “All of Us” Research Program. N. Engl. J. Med. 381, 668–676 (2019).

  3. 3.

    Torkamani, A., Wineinger, N. E. & Topol, E. J. The personal and clinical utility of polygenic risk scores. Nat. Rev. Genet. 19, 1–10 (2018).

  4. 4.

    Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50, 1505–1513 (2018).

  5. 5.

    Schumacher, F. R. et al. Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nat. Genet. 50, 928–936 (2018).

  6. 6.

    Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).

  7. 7.

    Duncan, L. et al. Analysis of polygenic risk score usage and performance in diverse human populations. Nat. Commun. 10, 3328 (2019).

  8. 8.

    Thun, M. J. et al. 50-Year trends in smoking-related mortality in the United States. N. Engl. J. Med. 368, 351–364 (2013).

  9. 9.

    Sotos-Prieto, M. et al. Association of changes in diet quality with total and cause-specific mortality. N. Engl. J. Med. 377, 143–153 (2017).

  10. 10.

    Stolberg, H. O., Norman, G. & Trop, I. Randomized controlled trials. Am. J. Roentgenol. 183, 1539–1544 (2004).

  11. 11.

    Nagai, A. et al. Overview of the BioBank Japan Project: study design and profile. J. Epidemiol. 27, S2–S8 (2017).

  12. 12.

    Hirata, M. et al. Overview of BioBank Japan follow-up data in 32 diseases. J. Epidemiol. 27, S22–S28 (2017).

  13. 13.

    Hirata, M. et al. Cross-sectional analysis of BioBank Japan clinical data: a large cohort of 200,000 patients with 47 common diseases. J. Epidemiol. 27, S9–S21 (2017).

  14. 14.

    Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

  15. 15.

    Fischer, K. et al. Biomarker profiling by nuclear magnetic resonance spectroscopy for the prediction of all-cause mortality: an observational study of 17,345 persons. PLoS Med. 11, e1001606 (2014).

  16. 16.

    Kunutsor, S. K., Apekey, T. A., Seddoh, D. & Walley, J. Liver enzymes and risk of all-cause mortality in general populations: a systematic review and meta-analysis. Int. J. Epidemiol. 43, 187–201 (2014).

  17. 17.

    Emerging Risk Factors Collaboration Adult height and the risk of cause-specific death and vascular morbidity in 1 million people: individual participant meta-analysis. Int. J. Epidemiol. 41, 1419–1433 (2012).

  18. 18.

    Ihira, H. et al. Adult height and all-cause and cause-specific mortality in the Japan Public Health Center-based prospective study (JPHC). PLoS ONE 13, e0197164 (2018).

  19. 19.

    Davey Smith, G. & Hemani, G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23, R89–98 (2014).

  20. 20.

    Burgess, S. & Thompson, S. G. Use of allele scores as instrumental variables for Mendelian randomization. Int. J. Epidemiol. 42, 1134–1144 (2013).

  21. 21.

    Dorresteijn, J. A. N. et al. Relation between blood pressure and vascular events and mortality in patients with manifest vascular disease: J-curve revisited. Hypertension 59, 14–21 (2012).

  22. 22.

    Rawshani, A. et al. Risk factors, mortality, and cardiovascular outcomes in patients with type 2 diabetes. N. Engl. J. Med. 379, 633–644 (2018).

  23. 23.

    He, J. et al. Premature deaths attributable to blood pressure in China: a prospective cohort study. Lancet 374, 1765–1772 (2009).

  24. 24.

    Lewington, S., Clarke, R., Qizilbash, N., Peto, R. & Collins, R. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet 360, 1903–1913 (2002).

  25. 25.

    Chen, G., McAlister, F. A., Walker, R. L., Hemmelgarn, B. R. & Campbell, N. R. C. Cardiovascular outcomes in framingham participants with diabetes: the importance of blood pressure. Hypertension 57, 891–897 (2011).

  26. 26.

    Zheng, W. et al. Association between body-mass index and risk of death in more than 1 million Asians. N. Engl. J. Med. 364, 719–729 (2011).

  27. 27.

    Ravnskov, U. et al. Lack of an association or an inverse association between low-density-lipoprotein cholesterol and mortality in the elderly: a systematic review. BMJ Open 6, e010401 (2016).

  28. 28.

    Bonaccio, M. et al. Age-sex-specific ranges of platelet count and all-cause mortality: prospective findings from the MOLI-SANI study. Blood 127, 1614–1616 (2016).

  29. 29.

    Ueshima, H. et al. Impact of elevated blood pressure on mortality from all causes, cardiovascular diseases, heart disease and stroke among Japanese: 14 year follow-up of randomly selected population from Japanese - Nippon data 80. J. Hum. Hypertens. 17, 851–857 (2003).

  30. 30.

    Gerdts, E. et al. Left ventricular hypertrophy offsets the sex difference in cardiovascular risk (the Campania Salute Network). Int. J. Cardiol. 258, 257–261 (2018).

  31. 31.

    Smith, G. D. & Hemani, G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23, 89–98 (2014).

  32. 32.

    Richardson, T. G., Harrison, S., Hemani, G. & Davey Smith, G. An atlas of polygenic risk score associations to highlight putative causal relationships across the human phenome. Elife 8, e43657 (2019).

  33. 33.

    Frieser, M. J., Wilson, S. & Vrieze, S. Behavioral impact of return of genetic test results for complex disease: systematic review and meta-analysis. Health Psychol. 37, 1134–1144 (2018).

  34. 34.

    Natarajan, P. et al. Polygenic risk score identifies subgroup with higher burden of atherosclerosis and greater relative benefit from statin therapy in the primary prevention setting. Circulation 135, 2091–2101 (2017).

  35. 35.

    Akiyama, M. et al. Characterizing rare and low-frequency height-associated variants in the Japanese population. Nat. Commun. 10, 4393 (2019).

  36. 36.

    McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016).

  37. 37.

    Browning, B. L. & Browning, S. R. Genotype imputation with millions of reference samples. Am. J. Hum. Genet. 98, 116–126 (2016).

  38. 38.

    Timmers, P. R. et al. Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances. Elife 8, e39856 (2019).

  39. 39.

    Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

  40. 40.

    Kanai, M. et al. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat. Genet. 50, 390–400 (2018).

  41. 41.

    Bulik-Sullivan, B. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

  42. 42.

    Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

  43. 43.

    Ligthart, S. et al. Genome analyses of >200,000 individuals identify 58 loci for chronic inflammation and highlight pathways that link inflammation and complex disorders. Am. J. Hum. Genet. 103, 691–706 (2018).

  44. 44.

    Sohail, M. et al. Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies. Elife 8, e39702 (2019).

  45. 45.

    Grambsh, P. M. & Therneau, T. M. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika 81, 515–526 (1994).

  46. 46.

    Robinson, M. R. et al. Genetic evidence of assortative mating in humans. Nat. Hum. Behav. 1, 0016 (2017).

  47. 47.

    Mega, J. L. et al. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet 385, 2264–2271 (2015).

  48. 48.

    Joshi, P. K. et al. Genome-wide meta-analysis associates HLA-DQA1/DRB1 and LPA and lifestyle factors with human longevity. Nat. Commun. 8, 910 (2017).

  49. 49.

    Burgess, S., Butterworth, A. & Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol. 37, 658–665 (2013).

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Acknowledgements

We thank all the participants of BBJ, UKB and FinnGen. We thank A. Palotie for his support for the data analysis of FinnGen, B. M. Neale and N. Baya for sharing and discussing their idea on LOGO, and A. R. Martin for the PRS analysis on UKB. We thank K. Yamamoto for supporting our analyses. This research was supported by the Tailor-Made Medical Treatment Program (the BBJ Project) of the Ministry of Education, Culture, Sports, Science, and Technology (MEXT), the Japan Agency for Medical Research and Development (AMED). The FinnGen project is funded by two grants from Business Finland (HUS 4685/31/2016 and UH 4386/31/2016) and nine industry partners (AbbVie, AstraZeneca, Biogen, Celgene, Genentech, GSK, MSD, Pfizer and Sanofi). The following biobanks are acknowledged for collecting the FinnGen project samples: Auria Biobank (https://www.auria.fi/biopankki/), THL Biobank (https://thl.fi/en/web/thl-biobank), Helsinki Biobank (https://www.terveyskyla.fi/helsinginbiopankki/), Northern Finland Biobank Borealis (https://www.ppshp.fi/Tutkimus-ja-opetus/Biopankki), Finnish Clinical Biobank Tampere (https://www.tays.fi/biopankki), Biobank of Eastern Finland (https://ita-suomenbiopankki.fi), Central Finland Biobank (https://www.ksshp.fi/fi-FI/Potilaalle/Biopankki), Finnish Red Cross Blood Service Biobank (https://www.bloodservice.fi/Research%20Projects/biobanking), Terveystalo Biobank Finland (https://www.terveystalo.com/fi/Yritystietoa/Terveystalo-Biopankki/Biopankki/). Y.O. was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (15H05911, 19H01021), AMED (JP19gm6010001, JP19ek0410041, JP19ek0109413, and JP19km0405211), the Takeda Science Foundation, and the Bioinformatics Initiative of Osaka University Graduate School of Medicine, Osaka University. M.Kanai was supported by a Nakajima Foundation Fellowship and the Masason Foundation.

Author information

S.S., Y.K. and Y.O. conceived the study. M.H., M.Kubo, K.M. and Y.M. collected and managed the BBJ samples. S.S., M.Kanai, M.A., N.M., A.T., M.Kubo., Y.K. and Y.O. performed data cleaning and statistical analysis on BBJ. M.Kanai performed statistical analysis on UKB. J.K., M.Kurki and M.Kanai performed data cleaning and statistical analysis on FinnGen. M.J.D. contributed to the overall study design and the FinnGen analysis. Y.O. supervised the study. S.S., M.Kanai, J.K., Y.K. and Y.O. wrote the manuscript.

Correspondence to Yukinori Okada.

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Peer review information Kate Gao was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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A full list of members and their affiliations appears in the Supplementary Note.

Extended data

Extended Data Fig. 1 A comparison between observational studies in BioBank Japan and UK Biobank.

Hazard ratios (HRs) from Cox proportional-hazard models for lifespan according to observed phenotypes in BioBank Japan (BBJ [n=179,066]; a) and UK Biobank (UKB [n=361,194]; b) are shown and compared. The boxes indicate the point estimates, and the horizontal bars indicate the 95% confidence interval. Boxes colored in blue (a) or green (b) indicate the nominal significance (P < 0.05) and the white-filled boxes indicate the statistical significance after correcting for multiple testing by the Bonferroni method. All the acronyms are described in Fig. 2.

Extended Data Fig. 2 The relationship between systolic blood pressure and HR for age at death in BioBank Japan.

The HR for age at death according to the observed systolic blood pressure in BBJ (n=179,066) is shown. The dotted lines represent the 95% confidence interval.

Extended Data Fig. 3 Sex-stratified association studies of PRS with lifespan across three cohorts.

The results of hazard ratios from sex-stratified Cox proportional-hazard models for lifespan, according to the PRS of the clinical phenotypes in (a) BioBank Japan (n=179,066), (b) UK Biobank (n=361,194), and (c) FinnGen (n=135,638) are shown. The boxes in blue indicate the point estimates in males, and those in red indicate the point estimates in females. The horizontal bars are the 95% confidence interval. For both sexes, we separately performed the fixed-effect meta-analyses of the association results from the three cohorts (d) by the inverse-variance method.

Extended Data Fig. 4 Trans-ethnic Mendelian randomization studies.

Shown are the results of two-sample Mendelian randomization studies with an inverse-variance weighted method to estimate the causal effect of biomarkers on lifespan in (a) BioBank Japan (n=179,066), (b) UK Biobank (n=361,194), and (c) FinnGen (n=135,638). We performed a fixed-effect meta-analysis of the association results from the three cohorts (d) by the inverse-variance method (ntotal=675,898), and displayed only nominally significant traits (9 out of 33 investigated traits). The circles indicate the point estimates, and the horizontal bars are the 95% confidence interval. Circles in colors indicate the nominal significance (P < 0.05) and the white-filled circles indicate the statistical significance after the Bonferroni correction for multiple testing. The size of the circles reflects the statistical significance in -log10(P).

Extended Data Fig. 5 The overlap of the variants constituting the PRSs between UK Biobank and BioBank Japan.

a, Among the variants constituting UK Biobank PRSs, the variants in blue did not exist in BioBank Japan variant dataset, those in green existed in BioBank Japan variant dataset, and those in pink were shared with or tagged (r2 > 0.8) by the variants constituting BioBank Japan PRSs of the same trait. To calculate r2 of LD, we used the LD reference panel from 5,000 randomly selected BioBank Japan individuals. Please note that the variants constituting PRSs from all the 10 sub-groups were concatenated in 20 traits with LOGO analysis. b, Among the variants constituting BioBank Japan PRSs, the variants in blue did not exist in UK Biobank variant dataset, those in green existed in UK Biobank variant dataset, and those in pink were shared with or tagged by the variants constituting UK Biobank PRSs of the same trait. To calculate r2 of LD, we again used the LD reference panel from 5,000 randomly selected BioBank Japan individuals. Please note that the variants constituting PRSs from all the 10 sub-groups were concatenated in all 33 traits with LOGO analysis.

Extended Data Fig. 6 A funnel plot for the effects of systolic blood pressure (sBP) PRS on lifespan, according to disease groups.

Sensitivity analyses of the effect of sBP PRS on the age at death. A funnel plot of the effects of sBP PRS on the age at death is shown by stratifying study participants into disease groups with at least 3,000 case samples (ntrait=22). The effect sizes from Cox proportional-hazard models are on x axis, and inverse standard errors (precision) are on y axis. A dotted line indicates the effect size from overall participants (n=179,066).

Extended Data Fig. 7 A definition of the three bins according to the PRSs.

A distribution of normalized sBP PRS and the stratification according to the quintiles. We defined the lowest, intermediate, and highest PRS bins according to the quintiles of PRS (first, 2-4th, and fifth, respectively). Each quintile bin was defined so as to have the same number of participants.

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Sakaue, S., Kanai, M., Karjalainen, J. et al. Trans-biobank analysis with 676,000 individuals elucidates the association of polygenic risk scores of complex traits with human lifespan. Nat Med (2020). https://doi.org/10.1038/s41591-020-0785-8

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